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Diagnostic accuracy of contrast-enhanced spectral mammography for breast lesions: A systematic review and meta-analysis.

ABSTRACT: Breast cancer diagnosis and staging is based on mammography, ultrasound, and magnetic resonance imaging (MRI). Contrast enhanced spectral mammography (CESM) has gained momentum as an innovative and clinically useful method for breast assessment. CESM is based on abnormal enhancement of neoplastic tissue compared to surrounding breast tissue. We performed a systematic review of prospective trial to evaluate its diagnostic performance, following standard PRISMA-DTA. We used a bivariate random-effects regression approach to obtain summary estimates of both sensitivity and specificity of CESM. 8 studies published between 2003 and 2019 were included in the meta-analysis for a total of 945 lesions. The summary area under the curve obtained from all the study was 89% [95% CI 86%-91%], with a sensitivity of 85% [95% CI 73%-93%], and a specificity of 77% [95% CI 60%-88%]. With a pre-test probability of malignancy of 57% a positive finding at CESM gives a post-test probability of 83% while a negative finding a post-test probability of 20%. CESM shows a suboptimal sensitivity and specificity in the diagnosis of breast cancer in a selected population, and at present time, it could be considered only as a possible alternative test for breast lesions assessment when mammography and ultrasound are not conclusive or MRI is contraindicated or not available.

SUBMITTER: Suter MB

PROVIDER: S-EPMC7375655 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Diagnostic accuracy of contrast-enhanced spectral mammography for breast lesions: A systematic review and meta-analysis.

Suter Matteo Basilio MB Pesapane Filippo F Agazzi Giorgio Maria GM Gagliardi Tania T Nigro Olga O Bozzini Anna A Priolo Francesca F Penco Silvia S Cassano Enrico E Chini Claudio C Squizzato Alessandro A

Breast (Edinburgh, Scotland) 20200610

Breast cancer diagnosis and staging is based on mammography, ultrasound, and magnetic resonance imaging (MRI). Contrast enhanced spectral mammography (CESM) has gained momentum as an innovative and clinically useful method for breast assessment. CESM is based on abnormal enhancement of neoplastic tissue compared to surrounding breast tissue. We performed a systematic review of prospective trial to evaluate its diagnostic performance, following standard PRISMA-DTA. We used a bivariate random-effe ...[more]

PMID: 32540554

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Project description:Objective: A limited number of studies have focused on the radiomic analysis of contrast-enhanced mammography (CEM). We aimed to construct several radiomics-based models of CEM for classifying benign and malignant breast lesions. Materials and Methods: The retrospective, double-center study included women who underwent CEM between November 2013 and February 2020. Radiomic analysis was performed using high-energy (HE), low-energy (LE), and dual-energy subtraction (DES) images from CEM. Datasets were randomly divided into the training and testing sets at a ratio of 7:3. The maximum relevance minimum redundancy (mRMR) method and least absolute shrinkage and selection operator (LASSO) logistic regression were used to select the radiomic features and construct the best classification models. The performances of the models were assessed by the area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI). Leave-group-out cross-validation (LGOCV) for 100 rounds was performed to obtain the mean AUCs, which were compared by the Wilcoxon rank-sum test and the Kruskal-Wallis rank-sum test. Results: A total of 192 women with 226 breast lesions (101 benign; 125 malignant) were enrolled. The median age was 48 years (range, 22-70 years). For the classification of breast lesions, the AUCs of the best models were 0.931 (95% CI: 0.873-0.989) for HE, 0.897 (95% CI: 0.807-0.981) for LE, 0.882 (95% CI: 0.825-0.987) for DES images and 0.960 (95% CI: 0.910-0.998) for all of the CEM images in the testing set. According to LGOCV, the models constructed with the HE images and all of the CEM images showed the highest mean AUCs for the training (0.931 and 0.938, respectively; P < 0.05 for both) and testing sets (0.892 and 0.889, respectively; P = 0.55 for both), which were significantly higher than those of the two models constructed with the LE and DES images in the training (0.912 and 0.899, respectively; all P < 0.05) and testing sets (0.866 and 0.862, respectively; all P < 0.05). Conclusions: Radiomic analysis of CEM images was valuable for classifying benign and malignant breast lesions. The use of HE images or all three types of CEM images can achieve the best performance.

Project description:ObjectiveTo evaluate if a deep learning model can be used to characterise breast cancers on contrast-enhanced spectral mammography (CESM).MethodsThis retrospective mono-centric study included biopsy-proven invasive cancers with an enhancement on CESM. CESM images include low-energy images (LE) comparable to digital mammography and dual-energy subtracted images (DES) showing tumour angiogenesis. For each lesion, histologic type, tumour grade, estrogen receptor (ER) status, progesterone receptor (PR) status, HER-2 status, Ki-67 proliferation index, and the size of the invasive tumour were retrieved. The deep learning model used was a CheXNet-based model fine-tuned on CESM dataset. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was calculated for the different models: images by images and then by majority voting combining all the incidences for one tumour.ResultsIn total, 447 invasive breast cancers detected on CESM with pathological evidence, in 389 patients, which represented 2460 images analysed, were included. Concerning the ER, the deep learning model on the DES images had an AUC of 0.83 with the image-by-image analysis and of 0.85 for the majority voting. For the triple-negative analysis, a high AUC was observable for all models, in particularity for the model on LE images with an AUC of 0.90 for the image-by-image analysis and 0.91 for the majority voting. The AUC for the other histoprognostic factors was lower.ConclusionDeep learning analysis on CESM has the potential to determine histoprognostic tumours makers, notably estrogen receptor status, and triple-negative receptor status.Key points• A deep learning model developed for chest radiography was adapted by fine-tuning to be used on contrast-enhanced spectral mammography. • The adapted models allowed to determine for invasive breast cancers the status of estrogen receptors and triple-negative receptors. • Such models applied to contrast-enhanced spectral mammography could provide rapid prognostic and predictive information.

Project description:Contrast-enhanced MRI (CE-MRI) is the most sensitive technique for breast cancer detection. Contrast-enhanced mammography (CEM) is emerging as a possible alternative to CE-MRI.PurposeTo evaluate the diagnostic performance of a low radiation dose contrast-enhanced mammography (L-CEM) in women with suspicious findings on conventional imaging compared to CE-MRI of the breast.Study typeProspective, single center.PopulationWomen with suspicious findings on mammography, tomosynthesis, or ultrasound, and no contraindications for L-CEM or CE-MRI. Eighty women were included.Field strength/sequence1.5 and 3T CE-MRI, standard protocol for breast, with dedicated coils, according to international guidelines. L-CEM was performed using a dedicated prototype.AssessmentThree, off-site, blinded readers evaluated the images according to the BI-RADS lexicon in a randomized order, each in two separate reading sessions. Histology served as a gold standard.Statistical testLesion detection rate, sensitivity, specificity, and negative and positive predictive values (NPV, PPV) were calculated and compared with multivariate statistics.ResultsIncluded were 80 women (mean age, 54.3 years ±11.2 standard deviation) with 93 lesions (32 benign, 61 malignant). The detection rate was significantly higher with CE-MRI (92.5-94.6%; L-CEM 79.6-91.4%, P = 0.014). Sensitivity (L-CEM 65.6-90.2%; CE-MRI 83.6-93.4%, P = 0.086) and NPV (L-CEM 59.6-71.4%; CE-MRI 63.0-76.5%, P = 0.780) did not differ between the modalities. Specificity (L-CEM 46.9-96.9%; CE-MRI 37.5-53.1%, P = 0.001) and PPV (L-CEM 76.4-97.6%; CE-MRI 73.3-77.3%, P = 0.007) were significantly higher with L-CEM. Variations between readers were significant for sensitivity and NPV. The accuracy of L-CEM was as good as CE-MRI (75.3-76.3% vs. 72.0-75.3%, P = 0.514).Data conclusionL-CEM showed a high sensitivity and accuracy in women with suspicious findings on conventional imaging. Compared to CE-MRI, L-CEM has the potential to increase specificity and PPV. L-CEM might help to reduce false-positive biopsies while obtaining sensitivity comparable to that of CE-MRI LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:589-595.

Project description:Background: We performed a network meta-analysis to compare the diagnostic accuracy of contrast-enhanced ultrasound (CEUS) and shear wave elastography (SWE) in differentiating benign and malignant lesions in different body sites. Methods: A computerized literature search of Medline, Embase, SCOPUS, and Web of Science was performed using relevant keywords. Following data extraction, we calculated sensitivity, specificity, positive likelihood ratio (LR), negative LR, and diagnostic odds ratio (DOR) for CEUS, and SWE compared to histopathology as a reference standard. Statistical analyses were conducted by MetaDiSc (version 1.4) and R software (version 3.4.3). Results: One hundred and fourteen studies (15,926 patients) were pooled in the final analyses. Network meta-analysis showed that CEUS had significantly higher DOR than SWE (DOR = 27.14, 95%CI [2.30, 51.97]) in breast cancer detection. However, there were no significant differences between CEUS and SWE in hepatic (DOR = -6.67, 95%CI [-15.08, 1.74]) and thyroid cancer detection (DOR = 3.79, 95%CI [-3.10, 10.68]). Interestingly, ranking analysis showed that CEUS achieved higher DOR in detecting breast and thyroid cancer, while SWE achieved higher DOR in detecting hepatic cancer. The overall DOR for CEUS in detecting renal cancer was 53.44, 95%CI [29.89, 95.56] with an AUROC of 0.95, while the overall DOR for SWE in detecting prostate cancer was 25.35, 95%CI [7.15, 89.89] with an AUROC of 0.89. Conclusion: Both diagnostic tests showed relatively high sensitivity and specificity in detecting malignant tumors in different organs. Network meta-analysis showed that CEUS had higher diagnostic accuracy than SWE in detecting breast and thyroid cancer, while SWE had higher accuracy in detecting hepatic cancer. However, the results were not statistically significant in hepatic and thyroid malignancies. Further head-to-head comparisons are needed to confirm the optimal imaging technique to differentiate each cancer type.

Dataset Information

Diagnostic accuracy of contrast-enhanced spectral mammography for breast lesions: A systematic review and meta-analysis.

Publications

Diagnostic accuracy of contrast-enhanced spectral mammography for breast lesions: A systematic review and meta-analysis.

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